The Value of Hydrologic Information in Stochastic Dynamic Programming Models of a Multireservoir System

Tejada-Guibert, José Alberto; Johnson, Sharon A.; Stedinger, Jery R.

doi:10.1029/95wr02172

Cited by 136 publications

(87 citation statements)

References 25 publications

(21 reference statements)

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“…Early explicit WRM approaches were based on conventional optimisation methods, such as Stochastic Linear Programming (Jacobs et al, 1995) and Stochastic Dynamic Programming (Tejada-Guibert et al, 1995). More recently, different EAs were used (e.g.…”

Section: Current Statusmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

521

317

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

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Section: Current Statusmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

521

317

View full text Add to dashboard Cite

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“…(Bhaskar and Whitlatch, 1980), 댐의 적절한 운영 규칙 모형을 제안하게 되었고 (Karamouz and Houck, 1982), 다목적 댐의 여러 목적 을 고려하여 댐 운영의 기준에 대해 생각하게 되었다 (Loganathan and Bhattacharya, 1990;Mohan and Kesakar, 1991). 점차 연구의 범위를 확대함에 따라 다 수 댐의 연계 운영에 초점이 맞추어져 병렬 저수지 최 적 운영에 대한 연구를 시작으로 (Sand, 1984), 여러 최 적화 기법을 이용한 다수 댐 연계 운영에 관한 연구가 이어졌고 (Can and Houck, 1984;Tejada-Guibert et al, 1995;Oliveira and Loucks, 1997), 다목적함수에 대한 가중치를 부여해 최적의 운영을 도출하는 방법들이 제 안되었다 (Randall et al, 1997;김승권과 박영준, 1998).…”

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Optimization of Multi-reservoir Operation with a Hedging Rule: Case Study of the Han River Basin

Ryu¹,

Chung²,

Lee³

et al. 2009

Journal of Korea Water Resources Association

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The major reason to construct large dams is to store surplus water during rainy seasons and utilize it for water supply in dry seasons. Reservoir storage has to meet a pre-defined target to satisfy water demands and cope with a dry season when the availability of water resources are limited temporally as well as spatially. In this study, a Hedging rule that reduces total reservoir outflow as drought starts is applied to alleviate severe water shortages. Five stages for reducing outflow based on the current reservoir storage are proposed as the Hedging rule. The objective function is to minimize the total discrepancies between the target and actual reservoir storage, water supply and demand, and required minimum river discharge and actual river flow. Mixed Integer Linear Programming (MILP) is used to develop a multi-reservoir operation system with the Hedging rule. The developed system is applied for the Han River basin that includes four multi-purpose dams and one water supplying reservoir. One of the fours dams is primarily for power generation. Ten-day-based runoff from subbasins and water demand in 2003 and water supply plan to water users from the reservoirs are used from "Long Term Comprehensive Plan for Water Resources in Korea" and "Practical Handbook of Dam Operation in Korea", respectively. The model was optimized by GAMS/CPLEX which is LP/MIP solver using a branch-and-cut algorithm. As results, 99.99% of municipal demand, 99.91% of agricultural demand and 100.00% of minimum river discharge were satisfied and, at the same time, dam storage compared to the storage efficiency increased 10.04% which is a real operation data in 2003.

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“…Karamouz and Vasiliadis [1992], Tejada-Guibert et al [1995], Mousavi et al [2004], and Labadie [2004] have made a thorough review of previous studies in this field. There have been relatively fewer studies focusing on the water quality aspects of reservoir operation.…”

Section: Introductionmentioning

confidence: 99%

Optimal reservoir operation considering the water quality issues: A stochastic conflict resolution approach

Kerachian

Karamouz

2006

Water Resources Research

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[1] In this study, an algorithm combining a water quality simulation model and a deterministic/stochastic conflict resolution technique is developed for determining optimal reservoir operating rules. As different decision makers and stakeholders are involved in reservoir operation, the Nash bargaining theory is used to resolve the existing conflict of interests. The utility functions of the proposed models are developed on the basis of the reliability of the water supply to downstream demands, water storage, and the quality of the withdrawn water. The expected value on the Nash product is considered as the objective function of the stochastic model, which can incorporate the inherent uncertainty of reservoir inflow. A water quality simulation model is also developed to simulate the thermal stratification cycle and the reservoir discharge quality through a selective withdrawal structure. The optimization models are solved using a new version of genetic algorithms called varying chromosome length genetic algorithm (VLGA). In this algorithm the chromosome length is sequentially increased to provide a good initial solution for the final traditional GA-based optimization model. The proposed stochastic optimization model can also reduce the computational burden of the previously proposed models such as stochastic dynamic programming (SDP) by reducing the number of state transitions in each stage. The proposed models which are called VLGAQ and SVLGAQ are applied to the 15-Khordad Reservoir in the central part of Iran. The results show that the proposed models can reduce the salinity of allocated water to different water demands as well as the salinity buildup in the reservoir.Citation: Kerachian, R., and M. Karamouz (2006), Optimal reservoir operation considering the water quality issues: A stochastic conflict resolution approach, Water Resour. Res., 42, W12401,

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The Value of Hydrologic Information in Stochastic Dynamic Programming Models of a Multireservoir System

Cited by 136 publications

References 25 publications

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Optimization of Multi-reservoir Operation with a Hedging Rule: Case Study of the Han River Basin

Optimal reservoir operation considering the water quality issues: A stochastic conflict resolution approach

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